System and method for automating scientific and engineering experimentation for deriving surrogate response data

ABSTRACT

The present invention provides a system and method for automatically deriving unique surrogate response data from experiment results in which inherent data loss occurs in a sufficient number of the samples to disallow quantitative effects estimation at the experimenter&#39;s desired level of confidence for statistical significance. In part, the unique surrogate response data sets of the present invention have one or more of four primary characteristics including: each is numerically analyzable; each may be readily or directly obtained when inherent data loss occurs; each provides a response value for an experiment trial; and each provides information on the effect of a change made to the process or system that would have been obtainable if the experiment samples had had no inherent data loss.

CROSS-REFERENCE TO RELATED APPLICATIONS

Under 35 U.S.C. §120 the present application is a continuation of U.S.patent application Ser. No. 12/758,769, filed Apr. 12, 2012, entitled“SYSTEM AND METHOD FOR AUTOMATING SCIENTIFIC AND ENGINEERINGEXPERIMENTATION FOR DERIVING SURROGATE RESPONSE DATA,” which is aContinuation-In-Part of U.S. Pat. No. 7,613,574, issued Nov. 3, 2009,entitled “SYSTEM AND METHOD FOR AUTOMATING SCIENTIFIC AND ENGINEERINGEXPERIMENTATION FOR DERIVING SURROGATE RESPONSE DATA,” aContinuation-In-Part of U.S. application Ser. No. 12/463,311, filed May8, 2009, entitled, “SYSTEM AND METHOD FOR AUTOMATING SCIENTIFIC ANDENGINEERING EXPERIMENTATION FOR DERIVING SURROGATE RESPONSE DATA,” andclaims the benefit of priority of U.S. Patent Application No.61/168,384, filed Apr. 10, 2009, entitled “SYSTEM AND METHOD FORAUTOMATING SCIENTIFIC AND ENGINEERING EXPERIMENTATION FOR DERIVINGSURROGATE RESPONSE DATA,” all of which are incorporated herein byreference.

FIELD OF THE INVENTION

The present invention relates generally to automating research,development, and engineering experimentation processes and work and morespecifically to providing a system and method for automatedexperimentation and automatically deriving unique surrogate responsedata from experiment results.

BACKGROUND OF THE INVENTION

The execution steps in most research, development, and engineeringexperiments generally involve manual operations carried out onunconnected technology platforms. The scientist or engineer works inwhat are essentially isolated technology islands with manual operationsproviding the only bridges. To illustrate, when there is a StandardOperating Practice (SOP) Guide for the experimental work, it is often anelectronic document, for example in Microsoft Word. The experimentalplan (Step 1) within the SOP Guide has to be transferred to the targetdevice (instrument, instrument platform, or component module forexecution (Step 2) by manually re-keying the experiment into thedevice's instrument control program (ICP)—the device's controllingapplication software. In a few cases the statistical analysis of results(Step 3 a) can be done within the ICP, but it is most often done withina separate statistical analysis software package or spreadsheet programsuch as Microsoft Excel. This also requires manually transferring theresults data from the ICP to the analysis software package. Reporting ofresults (Step 3 b) is usually carried out in Microsoft Word, andtherefore requires the manual transfer of all results tables and graphsfrom the separate statistical analysis software package. The manualoperations within the general execution sequence steps are presentedbelow. The isolated technology islands are illustrated in FIGS. 1 and 2.

FIG. 1 illustrates the manual tools and operations involved in carryingout a research and development experiment. In this work a statisticalexperiment design protocol is first generated, via step 12. Thisprotocol is developed manually and off-line using non-validated toolssuch as Microsoft Word. The protocol then must be approved, once againmanually and off-line, via step 14. When required, sample amounts arethen calculated using non-validated tools such as Microsoft Excel, viastep 16. Thereafter the samples are prepared, via step 18 and theexperiment is run on a target device, via step 20, for example, ahigh-performance liquid chromatograph (HPLC). Running the experimentrequires manually re-constructing the statistical design within thetarget device's ICP. When this software does not exist, or does notallow for full instrument control, the experiment must be carried out ina fully manual mode by manually adjusting instrument settings betweenexperiment runs.

FIG. 2 illustrates the manual tools and operations involved in analyzingthe data and reporting the results of the research and developmentexperiment, via step 22. The analysis and reporting of data isaccomplished by first statistically analyzing and interpreting theexperiment data, off-line, using non-validated tools such as MicrosoftExcel. Next, it is determined whether or not there is a need for moreexperiments, possibly using off-line generic Design of Experiments (DOE)software, via step 24. Then, data are entered and a report is written,via step 26. Finally, the report is archived, via step 28. As is seenfrom the above, the research, development, and engineeringexperimentation process involves a series of activities that arecurrently conducted in separate “technology islands” that require manualdata exchanges among the tools that are used for each activity. However,until now, no overarching automation technology exists that bringstogether all the individual activities under a singleintegrated-technology platform that is adapted to multiple devices anddata systems.

Method development activities encompass the planning and experimentalwork involved in developing and optimizing an analytical method for itsintended use. These activities are often captured in company StandardOperating Procedure (SOP) documents that may incorporate Food and DrugAdministration (FDA) and International Conference on Harmonization (ICH)requirements and guidances. Method development SOP documents include adescription of all aspects of the method development work for eachexperiment type (e.g. early phase analytical column screening, latephase method optimization, method robustness) within a framework ofthree general execution sequence steps: (1) experimental plan, (2)instrumental procedures, and (3) analysis and reporting of results. Theindividual elements within these three general steps are presentedbelow.

Step 1: Generate Experimental Plan

-   -   Select experiment type    -   Select target instrument    -   Define study variables:    -   analyte concentrations    -   instrument parameters    -   environmental parameters    -   Specify number of levels per variable    -   Specify number of preparation replicates per sample    -   Specify number of injections per preparation replicate    -   Integrate standards    -   Include system suitability injections    -   Define Acceptance Criteria

Step 2: Construct Instrumental Procedures

Define required transformations of the experiment plan into the nativefile or data formats of the instrument's controlling ICP software(construction of Sample Sets and Method Sets or Sequence and Methodfiles).

-   -   Specify number of injections (rows)    -   Specify type of each injection (e.g., sample, standard)

Step 3: Analyze Data and Report Results

-   -   Specify analysis calculations and report content and format    -   Carry out numerical analyses    -   Compare analysis results to acceptance criteria (FDA & ICH        requirements)    -   Specify graphs and plots that should accompany the analysis    -   Construct graphs and plots    -   Compile final report

The execution steps in analytical method development generally involvemanual operations carried out on unconnected technology platforms. Toillustrate, an SOP Guide for the development of an HPLC analyticalmethod is often an electronic document in Microsoft Word. Theexperimental plan (Step 1) within the SOP Guide has to be transferred tothe HPLC instrument for execution (Step 2) by manually re-keying theexperiment into the instrument platform's ICP—in the case of an HPLCthis is typically referred to as a chromatography data system (CDS). Ina few cases the statistical analysis of results (Step 3) can beperformed within the CDS, but it is most often carried out within aseparate statistical analysis software package or spreadsheet programsuch as Microsoft Excel. This also requires manually transferring theresults data from the CDS to the analysis software package. Reporting ofresults (Step 3) is usually carried out in Microsoft Word, and thereforerequires the manual transfer of all results tables and graphs from theseparate statistical analysis software package. The manual operationswithin the three general execution sequence steps are presented below.

Step 1—Experimental Plan

-   -   Development plan developed in Microsoft Word.    -   Experimental design protocol developed in off-line DOE software.

Step 2—Instrumental Procedures

-   -   Manually build the Sequences or Sample Sets and instrument        methods in the CDS.    -   Raw peak (x, y) data reduction calculations performed by the CDS        (e.g. peak area, resolution, retention time, concentration).

Step 3 a—Statistical Analysis

-   -   Calculated results manually transferred from the CDS to        Microsoft Excel.    -   Statistical analysis usually carried out manually in Microsoft        Excel.    -   Some graphs generated manually in Microsoft Excel, some obtained        from the CDS.

Step 3 b—Reporting of Results

-   -   Reports manually constructed from template documents in        Microsoft Word.    -   Graphs and plots manually integrated into report document.

It is realized that prior art systems in the area do not address theoverarching problem of removing the manually intensive steps required tobridge the separate technology islands. Similarly, it is also realizedfrom the prior art that inherent data loss is known to occur in samplingof experimental results to impact quantitative effect estimations andthereby degrade and typically render inaccurate statistical confidencesfrom experimental results. However, the prior art is not instructive inassisting in overcoming these problems to improve the accuracy oranalyzability of experimental results and sampling, nor is the prior artinstructing in overcoming deficiencies enabling one to develop a readilyobtainable solution to overcome inherent data loss, provide anidentifiable metric for separate experimental undertakings, or provideinformation about resulting effects where experimental samples containinherent data losses.

For instance, often trial runs of research and development (R&D)experiments may be carried out by making changes to one or morecontrollable parameters (as used herein such may include but not belimited to study factors, instrument settings, controllable parametersof instrumentation, a set of discrete process events, or otherexperimentation factors with other factors remaining constant (as usedherein the controlled portion of an experiment or experimental run ortrial) of a process or system and then measuring test samples obtainedfrom in-process sampling or process output. Typically, an objective of aresearcher in these undertakings is to identify and quantify the effectsof the parameter changes on the identified important process outputquality attributes or performance characteristics that are beingmeasured. The quantified effects can then be used to define theparameter settings that will give the desired process output results.

FIG. 3 illustrates a generalized flow diagram of a process in apredetermined process flow direction (305) consisting of four discreteelements (300): base material input (310), key reactant input (320),heating (330), and chemical reaction (340). For the avoidance of doubt,FIG. 3 and its related embodiments are foundational to the presentinvention herein. The flow diagram 300 also contains an in-processmeasurement step at 335 and a process endpoint measurement step at 350.In this generalized process 300 the base material element may have oneor more controllable parameters such as material feed rate or be of twoor more blended components including base material formulation forexample. In addition, the measurements of the quality attributes orperformance characteristics of interest may actually be taken within theprocess stream, as would be done by an in-process measurement system, oron the process output material.

The process 300 of FIG. 3 can similarly be analogized via a chemicalseparation process performed by instrumentation such as that of an HPLC.FIG. 4A is demonstrative of such an adaptation of the general processflow diagram 300 of FIG. 3 to that of an HPLC. In FIG. 4A, the flowdiagram 400 comprises three primary HPLC process elements: solventdelivery (410), sample injection (420), and a separation chamber (430).

In FIG. 4A, method development experiments may be performed oncontrollable parameters within the HPLC to identify the parametersettings that are optimum for the separation of a given mixture ofcompounds. In such experiments, one critical performance characteristicbeing measured, for example, may be the degree of separation of themixture into isolated pure individual compounds, as is further definedby the legend at 440. However, and more particularly in typicalpractical applications such as those within the pharmaceutical industry,the active pharmaceutical ingredient (API) and one or more degradantsand/or impurities in a drug product often represents a normal mixture ofcompounds for which an HPLC method must be developed. As is known frompractical applications under tradition methods, accurately measuring theamount of API in a test sample (or actual sample) with an HPLC wouldrequire that the instrument first separate the API from the degradantsand/or impurities.

As used herein, the term “impurities” are defined to include but not belimited to components of the drug product formulation, which may also betermed excipients, or contaminants that come from various points orstages in the process or even the product packaging of an affectedproduct or sample. For example, an impurity may be a plastic compoundfrom a product container that may contaminate the surface of the drugtablet for instance. By further example, a test sample may be adissolved tablet (i.e., the solid dosage form of the drug product) thatcontains the API and impurities. As used herein, the term “degradants”are defined as breakdown products of a sample API or impurity, i.e.,molecules which result from the decomposition of the API or impurity. Asan additional further example, a test sample may be a dissolved tablet(i.e., the solid dosage form of the drug product) that is subject tostress conditions which attempt to force the degradation of the API andimpurity compounds in the sample.

Therefore a critical HPLC method development experiment objective in atraditional practice application may include identifying the instrumentoperating conditions that separate the API from any degradants andimpurities in a test mixture to the degree required (i.e., accuracylevel) to accurately measure the API amount. Further in separationmethod development experiments, for example, some of the HPLC parametersettings used in the experiment trials can result in the inability toaccurately measure a critical performance characteristic, such ascompound separation. These issues are known to be a significantchallenge for researchers and commercial entities alike.

The consequences of these limitations realized by many in the field thenare the inherent data losses in one or more experiment trials which canthen result in the inability to quantitatively analyze the experimentresults and draw any meaningful conclusions.

FIG. 4B depicts an instrument hardware framework 450 associated with anHPLC instrument system. The HPLC framework 450 comprises several processelements with controllable parameters that can be experimentallyaddressed. The process elements include: solvent formulation and solventpH adjusted using a controllable valve module for solvent selection(CVM—Solvent Switching) (451), the solvent flow rate (Pump Module)(452), the type of separation column adjusted using a controllable valvemodule for column switching (CVM—Column Switching) (453), a sampler(454) and a detector (455).

For FIG. 4B, a typical experiment (i.e., method development experiment)may be comprised of conducting one or more trials where a trial consistsof operating the HPLC instrument at one or more predetermined settingsof the study parameters, injecting a small amount of the sample mixtureinto the solvent stream and measuring critical performancecharacteristics such as the degree of separation of the individualsample compounds at the endpoint of the process 455.

By exemplar, objectives of experimentation under the framework of FIG.4B in view of the process set forth in FIG. 4A, may include attemptingto separate out one or more APIs from impurities or degradants. In suchexperiments, for example, the controllable parameters of the CVM modules(451 and 453) and the Pump Module (452) may be selected for experimentalstudy. In such experiments, CVM solvent switching parameters may beadjusted between experiment trials to deliver a solvent mix at adifferent pH and the results captured. In such experiments, CVM columnswitching parameters may also be adjusted so as to employ a differentcolumn, for example, in each experimental trial undertaken. Similarly,in such experiments, pump module parameters may be adjusted betweentrials to both change the rate at which the solvent formulation ischanged (i.e., proportion of organic solvent increased) during a trialrun and to deliver the solvent formulation at a different flow rate.However, as will become further evident, in these types of experiments,despite the objectives of experimentation including attempts to separateout one or more APIs from impurities and/or degradants by selectingpredetermined controllable parameters for experimental study, theresults can be inaccurate.

FIG. 4C depicts a graphical chromatogram representation 460 ofexperimental results data obtained from a particular trial run trial inone of the experimental runs under assessment herein (e.g. trial run11), wherein the “raw” results depicted in the figure are in the form of“absorbance peaks.” A peak typically occurs when a compound absorbslight transmitted through the solvent stream and is detected by thedetector as the compound passes the detector at a given time X, whereinthe baseline condition represents zero absorbance of the light.

As used herein, an “absorbance peak” or “peak” generally means avertical spike (Y axis deviation) along a horizontal line in the graphfrom baseline conditions (where Y=zero) occurring at a given X axis timeinterval. As also used herein, a compound's “retention time” is definedas the time from injection to detection, and, in the chromatogram, thistime is the X-axis value corresponding to the peak's maximum Y value.

In FIG. 4C, poorly separated peaks are apparent at 461 and 462.Interpretatively, each peak in FIG. 4C corresponds to at least onecompound (i.e., the API or an impurity). It should also be readilyrecognized that the area under a given peak is proportional to theamount of absorbed light, which is in turn proportional to the amount ofthe corresponding compound in the solvent stream passing the detector atthe time indicated on the X axis in the chromatogram.

However, problematically, translating the measured area of a given peakinto an amount of the corresponding compound is typically accurate onlywhere the peak in a chromatogram is the result of only one compound. Asa result, accurately measuring the amount of an individual compound in asample using traditional approaches is difficult and often impossiblewhen two or more compounds pass through the detector at the same timedue to lack of separation (i.e., 461 and 462). Unfortunately, theoccurrence of two or more compounds passing through the detector at thesame time due to lack of separation is quite a common event in manymethod development experiment instances.

To attempt to compensate for this limitation, often a primary goal ofmany HPLC method development experiments is to identify the instrumentsettings that result in a chromatogram with the following criticalcharacteristics: (1) an observable peak being present for each compoundin the sample, (2) situations where each peak is separated from allother peaks (i.e., no overlap) to a degree at least minimally necessaryto accurately quantify the amount of the corresponding compound in thesample, and (3) the separation of a critical peak, often an API, fromits nearest peak. The degree of separation between a given pair ofadjacent compound peaks in a chromatogram is defined herein as the “peakresolution.”

In a traditional approach to HPLC method development, the effect ofinstrument setting changes on the resolution of sample compounds istherefore typically relied on as being one of the most importantexperiment results. As a result, it is traditionally believed andpracticed to carry out the following steps:

-   a. change one or more instrument settings, inject a sample, and    obtain a resulting chromatogram;-   b. associate each peak in the chromatogram with one of the sample    compounds;-   c. compute the peak resolution results for all adjacent peak    (compound) pairs;-   d. determine if the compounds are sufficiently separated, as    represented by the adjacent peak pair resolution data, to accurately    determine the amount of each compound in the sample to the required    level of precision; and-   e. repeat Steps (a)-(d) above if the compounds are not sufficiently    resolved.

Unfortunately, the correct assignment of the sample compounds to thechromatogram peaks as in Step (b) above is critical to accuratelyinterpret experiment trial results in accordance with traditionalpractice. Such traditional practice characteristics may include currentnumerical analysis approaches and the like. Since, as is often thesituation, current analysis and interpretation approaches target theinteractions of each compound with the HPLC system elements that resultfrom the specific chemical and structural nature of the compound,determining specifically and precisely which compound each resolutionresult associates with, in a given chromatogram, is effectively the onlyway to track the effects of instrument changes on the separation of thatcompound.

A further complication especially common to early HPLC methoddevelopment experiments that involve analytical column and pH screeninghas been that it may not be readily determinable as to how manycompounds are in an experimental sample, and therefore how many peaks anexperimenter is to expect in a chromatogram obtained from sampleanalysis by HPLC. This particular complication is further illustrated bycomparing FIG. 4C with FIG. 4D.

FIG. 4D is a chromatogram 470 obtained from the same sample of FIG. 4Cas analyzed under different trial settings of the HPLC instrument. Thechromatogram of FIG. 4D shows twelve well separated peaks being visiblealong the X axis time interval of 10 to 34 minutes (see for examplerepresentative peaks at 471 and 472, where an uncertain or undefinablenumber of peaks exist in this same interval in FIG. 4C (see for examplerepresentative points at 461 and 462).

However, additional complications can result even where the number andidentity of all compounds in a test sample are known as such knowledgedoes not necessarily simplify the work of correctly associating eachpeak with a sample compound in each trial chromatogram, since instrumentchanges between trials can affect both peak shape (i.e., broad-flatversus narrow-spiked) and the column transit time of the correspondingcompound (i.e., peak retention time).

For example, for a particular experimental trial, a peak arising in aresulting chromatogram corresponding to a given compound may occur at 15minutes and appear narrow and spiked. In a second trial with differentinstrument settings, the peak corresponding to the same compound mayoccur at 12 minutes and may appear as being broad and flat.Contradistinctively, a third trial's settings may cause a second peak toalso occur at the 12 minute location in the chromatogram resulting in acombined peak that differs greatly in shape and area from the others. Byfurther example, in FIG. 4C at 461, overlapping peaks corresponding toincompletely separated compounds can be seen, and again at 462, whilepeaks with the same or very similar shape and area in FIG. 4D occur atapproximately 22, 23, and 24 minutes (473, 474, and 475 respectively).

Exemplary Experimental Data

FIG. 13 is a table that presents a data set from an experiment todevelop a HPLC method for a drug product sample containing two APIs andseveral impurities. In the data set of FIG. 13 the peak resolutionresponses are used directly in data analysis according to the currentpractice (i.e., traditional) approach. As used herein, it is understoodthat the standard calculation of resolution for a given compoundrepresents the normalized distance (i.e., degree of separation) of thecompound's peak from the peak directly in front of it in the solventstream, which corresponds to the peak directly to the left of thesubject in the chromatogram, since that peak has an earlier X-axis timepoint. Therefore, for example, in the data set presented in FIG. 13, the“3—Resolution” column response represents the degree of separation ofCompound 3 from Compound 2 (where Compound 2 is the compound directlyahead of it in the solvent stream). Similarly, the “4—Resolution” columnresponse represents the degree of separation of Compound 4 from Compound3, and the remaining columns of FIG. 13 are similarly defined.

As becomes apparent from FIG. 13, notably absent are numerous resolutionresult values in the data set for Compounds 3 and 4 a—two impuritiesthat must be able to be separated from Compounds 4 and 5, the two APIsin this drug product sample. The trials in which the resolution valuesfor these impurities are missing correspond to instrument settings whichwere unable to separate the impurities from the APIs. This assessment isvisible for Compound 4 a when compared with the chromatograms in FIGS.4E and 4F, which correspond to the results obtained from two distinctexperiment trials, 11 and 12 respectively, as identified in FIG. 13.FIG. 4E is a chromatogram 479 resulting from an experiment run of trial11, of which there is no peak corresponding to Compound 4 a therein.FIG. 4F is a chromatogram 485 resulting from an experiment run of trial12.

FIG. 4G is a chromatogram 490 resulting from an experiment run of trial22. By comparison of FIGS. 4F and 4G, the differences between thechromatograms illustrate an entirely different kind of inherent dataloss that also severely compromises the current practice approach. Inthis comparative assessment, both trials represent instrument conditionsin which Compound 4 a is resolved. However, the resolution result intrial 12 (FIG. 4E) is a measure of the separation of Compound 4 a fromCompound 3; while in trial 22 (FIG. 4F) the result is a measure ofCompound 4 a separation from Compound 5 (in part due to Compounds 3 and5 overlapping in this particular trial).

Unfortunately, this type of resulting change in what the data representacross trials, which represents inherent loss in terms of informationcontent of the data, is a common consequence of the change in peaklocations in response to the changing instrument settings, andrepresents a challenging problem.

The result of inherent data loss in HPLC method development experimentalwork is that the data typically do not accurately represent a compound'sactual chemistry-based behavior, and, as a consequence, provide doubttowards legitimate analysis and accurate interpretation of the results.This impact to the integrity of the results is further observable viaregression analysis (equation-fitting) of the Compound 4 a data, theresults of which are set forth in FIG. 14. FIG. 14 is a table of the keyregression statistics obtained from linear regression analysis of theFIG. 13 study factor data and associated compound 4 a Resolutionresults.

For instance, the R2-Adj. (see “Adj. R Square”) in FIG. 14 is thecritical measure of equation predictive accuracy. The value of 0.0639 inFIG. 14 is not statistically different from zero, thereby meaning thatthe equation has no or questionable predictive accuracy. However, thestudy parameters included Column Type (e.g., two very different columns)and a wide range of Final % Organic (i.e., the gradient endpoint percentorganic solvent)—two instrument parameters known to greatly affectcompound separation under almost all conditions. Additionally, theobserved changes in the resolution data across trials are substantiallygreater than can be accounted for by HPLC operating error.

Therefore, it can be and is readily determined that inherent data lossis often the cause of the inability to derive statistically validresults from numerical analysis of current practice data.

The problems described here are systemic to current HPLC methoddevelopment experiment practice. In part the complications andlimitations of the traditional approach start the method developmentprocess by studying the factors known or expected to have the greatestaffect on peak shape and compound retention time, and therefore peakseparation. However, this traditional approach results in changes thatmake correct compound assignments between trials extremely difficult andchallenging. As a result, critical information sought from theexperiment is normally not readily available due to the limitationsinherent in the practice itself.

SUMMARY OF THE INVENTION

The present invention addresses such a need and sets forth an approachto solve these issues by deriving unique surrogate response data fromexperiment results which, in the case of the HPLC method developmentexample just described, eliminates the need for assigning samplecompounds to chromatogram peaks in each experiment trial, therebyeliminating the inherent data loss associated with the current practice.

A system and method for automatically deriving unique surrogate responsedata from experiment results in which inherent data loss occurs in asufficient number of the samples to disallow quantitative effectsestimation at the experimenter's desired level of confidence forstatistical significance is disclosed.

In part, the unique surrogate response data sets generated by thepresent invention have four primary characteristics including: each isnumerically analyzable; each may be more readily or directly obtainedthan the current practice results data in which inherent data lossoccurs; each provides a response value for an experiment trial; and eachprovides information on the effect of a change made to the process orsystem that would have been obtainable if the experiment samples had hadno inherent data loss.

In one embodiment the present invention is an automated system forreducing inherent data loss associated with experimentation whichautomatically derives one or more unique surrogate response data forexperimentation. The system comprises an automated experimentationplatform (AEP) for automating one or more experimentation processes, ageneralized exchange module (GEM) for automating data exchanges betweenthe said AEP and one or more target applications and for enabling thedata exchange to be generic with one or more attached componentsincluding any of instrumentation, device, software application or ICP,and a means for selectively predetermining study factors for saidexperimentation.

In a preferred embodiment, the present invention is utilized with anHPLC system. In this preferred embodiment, the present invention is amethod for reducing inherent data loss associated with experimentationcomprising automatically deriving one or more surrogate response datafor a defined set of experimentation having the steps of: automating oneor more discrete experimentation process steps; automating dataexchanges between an AEP and one or more target applications, forenabling the data exchange to be generic with one or more of anyattached components inclusive of any hardware, software, HPLC system,software applications and ICP, and generating surrogate responses inrelation to controllable parameters in relation to study factors forsaid experimentation.

In a further preferred embodiment the method additionally includesfurther the step of executing a second experimentation trial in whichthe study factors used in the first experiment are held constant at thelevel settings defined as optimum by analysis of that experiment's TrendResponse data set. --The second experimentation trial is conducted so asto further optimize the HPLC instrument analytical method.

In yet another preferred embodiment a computer readable mediumcontaining program instructions is provided to implement the presentinvention.

As a foundation, the system in an aspect of the present invention,comprises an automated experimentation platform (AEP) with a devicesetup interface that imports device setup and control definitions andallows user configuration and editing of the definitions, an experimentsetup interface that is dynamically configurable to specific experimenttypes and their target instrument platforms and devices, and allows userfinal configuration and editing of all experiment setup settings, areporting setup interface that dynamically builds reports from data andresults and allows user configuration and editing of the reports, adesign of experiments (DOE) engine that generates statistically validand rigorous scientific experiments and sampling plans tailored to thetarget devices, including any software program for controlling thedevices (ICP); and a generalized exchange module (GEM) for automatingdata exchanges between the AEP and one or more target applications andfor enabling the data exchange to be generic.

Through the use of the automated experimentation platform (AEP) andgeneralized exchange module (GEM) data exchange is automaticallyprovided between the DOE engine and the instrument, device, or (ICP);and through the generalization of the exchange module the data exchangescan be adapted to any external software application, instrument, device,or ICP. Therefore, configuring of any scientific experiment type,control of any instrument or device, reporting of any data and results,and data exchanges between external software applications, instrumentplatforms, and devices can be achieved by the AEP and GEM automationcomponents of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the manual tools and operations involved in designinga research, development, or engineering experiment.

FIG. 2 illustrates the manual tools and operations involved in analyzingthe data and reporting the results of a research, development, orengineering experiment.

FIG. 3 illustrates a generalized flow diagram of a process in a processflow direction consisting of four discrete elements: base materialinput, key reactant input, heating, and chemical reaction.

FIG. 4A demonstrates the adaptation of the general process flow diagram300 of FIG. 3 to that of an HPLC.

FIG. 4B depicts an instrument hardware framework associated with an HPLCinstrument system.

FIG. 4C depicts a graphical chromatogram representation of experimentalresults data obtained from a particular trial run trial in one of theexperimental runs under assessment herein, wherein the “raw” results inthis chromatogram are in the form of “absorbance peaks”.

FIG. 4D is a chromatogram obtained from the same sample of FIG. 4C asanalyzed under different trial settings of the HPLC instrument.

FIG. 4E is a chromatogram resulting from trial run 11 of an experiment.

FIG. 4F is a chromatogram resulting from trial run 12 of an experiment.

FIG. 4G is a chromatogram resulting from trial run 22 of an experiment.

FIG. 5 provides an automated experimentation platform (AEP) andgeneralized exchange module (GEM) operational flow diagram illustrating(1) XML—based data exchange with a third-party company as one mechanismof fileless data exchange, and (2) resulting AEP process flows for thesurrogate data generator with an instrument control program (ICP), in apreferred embodiment of the present invention.

FIGS. 6-8 show different software installation configurations of anautomated experimentation system with an ICP.

FIG. 9 illustrates the research, development, or engineering experimentworkflow previously presented in FIG. 1 adapted to an HPLC methoddevelopment experiment created within the automated experimentationsystem and automatically transferred to an instrument's ICP.

FIG. 10 illustrates the research, development, or engineering experimentworkflow previously presented in FIG. 2 adapted to an HPLC methoddevelopment experiment in which the results are generated by the ICP andautomatically imported into the automated experimentation system forautomated analysis graphing, and reporting.

FIG. 11 is an example of a software dialog enabling the user to selectone or more operators and value settings which together define thedesired surrogate responses to be derived from the experiment results,referred to as Trend Responses in this embodiment and figure.

FIG. 12 presents the operational functional flow of the invention in apreferred embodiment including the HPLC method development experiment.

FIG. 13 is a table that presents a data set from an experiment todevelop a HPLC method for a drug product sample containing two APIs andseveral impurities.

FIG. 14 is regression statistics for compound 4 a.

FIG. 15 presents some of the Trend Response data computed in anexperimental use scenario of the present invention implementing FusionAE from the HPLC method development experiment discussed previously.

FIG. 16 presents the regression analysis results for the Total Peakstrend response.

FIG. 17 presents the regression analysis results for the Resolved Peaks(>1.50) trend response.

DETAILED DESCRIPTION

Abbreviations and Acronyms

AEP—Automated Experimentation Platform.

CDS—Chromatography Data System. A traditional name for an ICP (seebelow) that controls and handles the data from HPLC and GC instruments(see below).

DOE—design of experiments

GEM—generalized exchange module.

Device—An instrument, piece of equipment, or apparatus. Examples includebut are not limited to analytical instruments, weighing and measurementdevices, sampling and sample handling equipment, and processingequipment.

FDA—United States Food and Drug Administration.

GC—Gas Chromatography. An instrument-based quantitative analyticaltechnique. GC instruments are widely used as research and developmentand quality assurance tools in many industries (e.g. pharmaceuticals,biotechnology, and petrochemicals).

HPLC—High Performance Liquid Chromatography. An instrument-basedquantitative analytical technique. HPLC instruments are widely used asresearch and development and quality assurance tools in many industries(e.g. pharmaceuticals, biotechnology, and petrochemicals).

ICH—International Conference on Harmonization of Technical Requirementsfor Registration of Pharmaceuticals for Human Use.

ICP—instrument control program. The software program that operates adevice.

Programmatic Interface—A software based communication channel thatallows software programs to exchange instructions and data.

SDK—Software Development Kit. An SDK is a published programmaticinterface for a device or a device's ICP.

XML—eXtensible Markup Language. A metalanguage written in SGML thatallows one to design a markup language, used to allow for the easyinterchange of documents on the World Wide Web.

XML Schema—XML Schemas express shared vocabularies and allow machines tocarry out rules made by people. They provide a means for defining thestructure, content and semantics of XML documents.

XSL—eXtensible Stylesheet Language. A language used to construct stylesheets for an XML, consisting of two parts:

-   -   1. XSL Transformations (XSLT). A language for transforming XML        documents.    -   2. XSL Formatting Objects (XSL FO). XML vocabulary for        specifying formatting semantics.

XSL Transformation—The set of XSL templates applied to XML data totransform it into another format.

XSL File—The file that contains the XML Transformation.

XSD File—XML Schema Definition is a language for specifying the grammarof the markup allowed in an XML file. Such a specification is called aschema and typically has a file extension of XSD.

The present invention relates generally to scientific research,development, and engineering experiments and more specifically toproviding a system for automated experimentation. The followingdescription is presented to enable one of ordinary skill in the art tomake and use the invention and is provided in the context of a patentapplication and its requirements. Various modifications to the preferredembodiments and the generic principles and features described hereinwill be readily apparent to those skilled in the art. Thus, the presentinvention is not intended to be limited to the embodiments shown, but isto be accorded the widest scope consistent with the principles andfeatures described herein.

In one aspect, foundationally, a system and method in accordance withthe present invention provides for full automation of research,development, and engineering experimental work. The present invention insuch an aspect provides for:

-   -   1. Creating and exchanging device setup and control definitions,        experiment type definitions, analysis definitions, reporting        definitions, and user addressable configuration and control of        the definitions.    -   2. An experiment setup interface that dynamically configures to        specific experiment types and their target instrument platforms        and devices.    -   3. Automating data exchange between a design of experiments        (DOE) software engine and any targeted software application,        instrument, device or ICP.    -   4. Making the data exchange technology generic and adaptable to        any targeted software application, instrument, device, or ICP.

Aspects of the present invention utilize the automated experimentationprocess disclosed in U.S. patent application Ser. No. 11/262,539, filedon Oct. 25, 2005, entitled “System for Automating Scientific andEngineering Experimentation,” which is incorporated herein by reference.

To describe the features of the present invention in more detail, refernow to the following description in conjunction with the accompanyingFigures.

Overview

An automated experimentation (AE) system in accordance with the presentinvention is a proprietary software platform for automatedexperimentation. In a preferred embodiment, the AE system comprises asoftware program with statistical and mathematical informatics enginesfor:

-   -   Design of experiments (common abbreviations: DOX, DOE)    -   Numerical data analysis    -   2D, 3D, and 4D (trellis) visualization graphics    -   Multiple response optimization    -   Formal reporting

The AE system also includes tools for regulatory (FDA, ICH) compliance,workflow management control, application-specific experimentation, andfile-less data exchange with targeted software applications,instruments, devices, or ICPs.

The AE system exports experiment designs to targeted softwareapplications, instruments, devices, or ICPs as ready-to-run experimentsin the native file and data formats of the target, imports all resultsfrom the target, analyzes and graphs the results, and createspresentation quality reports.

In a currently available example embodiment the AE system's strategicfeatures include:

E-lab Notebook Interface—Document style interface displays reports inHTML windows. Encrypted database file format contains OLE objects forembedding a Microsoft® Word™ document and Microsoft Excel™ workbook.Externally generated graphics can be imported and embedded into allreports.

Full 21 CFR 11 Compliance Support toolset—Includes e-signature controlsfor all data entry and exchanges, full audit trail, and event logging.

Analysis and Reporting—Application-specific automated statisticalanalysis, graphing, and reporting.

Acceptance Criteria Testing—Embedded analytics automatically compareactual results with user entered “pass/fail” acceptance criteria.

Workflow Management—Construct and export work templates. Permissions andauthorities control of all work.

In the pharmaceutical and biotechnology industries, current embodimentsof the AE system are utilized in the mission-critical activities withinanalytical R&D, chemical entity development (CED), chemistry development(CRD), process R&D, formulation R&D, and manufacturing QA.

Accordingly, the AE system in an example embodiment of the presentinvention is a software system which utilizes custom application modulesfor the targeted design of experiments and the analysis, graphing,optimization, and reporting of experimental data and results in supportof all drug development pipeline activities.

The AE system imports Device Driver XML files (Device XMLs) generatedoutside of the AE system according to a public Schema, for example anXML schema. Device XMLs contain the data that enables the AE system toset up experiments and address, control, and exchange data with one ormore devices. These XML files contain the instructions that allow the AEsystem to address the existing interface of one or more devices or theircontrolling ICPs.

User generated Device XMLs enable the AE system to address and controlany instrument platform, component module, or device via any public orprivate programmatic interface that the platform module, or devicecontains. No programming need be developed to adapt the existinginterface of an external software program, device, or ICP to the AEsystem.

The AE system utilizes experiment type XML files (experiment XMLs)generated outside of the AE system according to a public Schema, forexample an AE XML schema. The experiment XMLs contain the data thatenable the AE system to dynamically configure the experiment setupinterface to address the specific experiment type and its targetdevices. The AE system automatically applies an experiment setup builderXSL to the experiment XML and the device XML to generate an experimentsetup XML that complies with the AE XML Schema. No programming need bedeveloped to adapt the experiment type to the AE system. An AE graphicaluser interface (GUI) builder transforms experiment type settingsdescriptions and device descriptions into AE's dynamically configurableAE DOE GUI. The GUI displays all experiment type settings along withdevice control points, constraints, and graphical images of each controlpoint, for final refinement. The AE DOE GUI enables construction ofstatistically designed experiments for automatic execution on the deviceusing the final experiment type and device settings description.

The AE system utilizes analysis template XML files (Analysis XMLs)generated outside of the AE system according to a public Schema, forexample an AE XML schema. The analysis XMLs contain the data thatenables the AE system to dynamically select and sequence the analysisroutines from its internal analysis library that are applied to data andresults to generate an analysis results set template specific to theexperiment type, the user requirements, and the area of application. Theanalysis results set can be automatically available to any report byincluding it into the report XML. No programming need be developed toadapt analysis templates to the AE system. An AE analysis buildertransforms analysis routine and sequence settings descriptions into AE'sdynamically configurable data analyzer GUI. The GUI displays all routineand sequence settings for final refinement. The data analyzer GUIenables automatic data analysis on the AE System using the finalanalysis settings description.

The AE system utilizes report template XML files (Report XMLs) generatedoutside of the AE system according to a public Schema, for example an AEXML schema. The Report XMLs contain the data that enables the AE systemto dynamically configure the reporting engine to generate a reportspecific to the experiment type, the user requirements, and the area ofapplication. No programming need be developed to adapt report templatesto the AE system. An AE report builder transforms analysis report dataand results complement and sequence settings descriptions into AE'sdynamically configurable reporter GUI. The GUI displays all complementand sequence settings for final refinement. The reporter GUI enablesautomatic report generation on the AE System using the final reportsettings description.

The public XML schema enables the user to update, add to, remove, orotherwise modify the device XMLs, experiment XMLs, analysis XMLs, andreport XMLs created for external software applications, instrumentplatforms, devices, or ICPs at any time to dynamically address changesto the target application platform, device, or ICP.

In addition, in a preferred embodiment, the AE system includes a varietyof plug-in application modules—each of which generates specific types ofstatistically-based experiment designs as directed by experiment typeXMLs and device XMLs, and executes the associated analysis, graphing,optimization, and reporting of the experiment's results, as directed byanalysis XMLs and report XMLs. The user could configure and direct theapplication modules, for example, in a preferred embodiment, byconstructing device XMLs, experiment XMLs, analysis XMLs, and reportXMLs, and operate the application module tools through a series ofrule-based wizards.

AE system application modules can address a wide variety of differentapplication areas. For example, one application module is used forchromatographic analytical instrument method development while anotheris used for synthetic chemistry process development.

The device XMLs of the AE system also preferably contain the data thatenables the AE to address, control, and exchange data with variousindependently conceived and separately designed target softwareapplications, instrument platforms, devices, and ICPs via any public orprivate programmatic interface that the target contains.

The AE system in a preferred embodiment is an electronic signature basedsystem for enabling software operations and subroutines and imposingmanagement review and approve loops on user work within the AE system.

The AE system generates statistical experiment designs or userconstructed experiments that can be run on the target instrument,directly or via the instrument's controlling ICP. The AE systemcommunicates the experiments to an instrument's controlling software viafile-less data transfer using the instrument's public or privateprogrammatic interface.

In addition, driver XML files of the AE system contain several layers ofdata regarding constraints on controllable instrument parameters,including:

-   -   Absolute constraints: achievable setting limits.    -   Manager constraints: restrictions on setting limits due to        current state or required practice.    -   Analyst constraints: restrictions based on current experiment        considerations.

The AE system includes an automated experimentation platform (AEP) and ageneralized exchange module (GEM) to provide a unified software platformfor use in automating experiments. To describe the features of GEM andits interaction with the other AE system elements refer now to thefollowing description in conjunction with the accompanying Figures.

Overview—Generalized Exchange Module (GEM)

The generalized exchange module (GEM) is a proprietary software-basedtechnology that enables a software program to dynamically configure itsuser interface and directly control a device and/or directly address thedevice's ICP—whether or not the device's target programmatic interfaceis published in an SDK. The level of control that can be provided by GEMis only limited by the level of device addressability provided by thedevice's programmatic interface. GEM accomplishes this through thefollowing program elements:

GEM XML schema by which users can completely describe for any ICP:

-   -   All controllable elements of a device (modules and sub-modules).    -   Graphical images of each device element.    -   Individual control points of each device element.    -   Dependency relationships between elements and control points of        a device.    -   Constraints (limits and other restrictions) on the allowable        settings of control points.    -   Programmatic commands, addresses, and data paths for controlling        the device.    -   Programmatic commands, addresses, and data paths for retrieving        data from the device.

The following components are utilized in a preferred embodiment of aGEM:

-   -   An ICP importer that transforms an ICP's native device        descriptions into GEM's native data structure.    -   A template importer that transforms experiment type settings        descriptions, analysis settings descriptions, and report        settings descriptions into the AEP's native data structure.    -   A GEM GUI builder that transforms device descriptions into GEM's        dynamically configurable GEM ICP GUI. The GEM ICP GUI displays        all device control points and constraints, including graphical        images of each control point for further refinement and        restriction of the device description.    -   A design of experiment (DOE) exporter that writes the control        point settings of the statistical experiment design or user        constructed experiment to the device.    -   A DOE importer that reads experiment result output data from the        described device.

FUNCTION of GEM

-   -   Transforming an ICP's native device descriptions into GEM's        native data structure.

Displaying all device control points and constraints, includinggraphical images of each control point via a configurable ICP userinterface.

-   -   Transforming experiment type, analysis, and reporting settings        descriptions into the AEP's native data structure.    -   Indirect or direct control of a device and/or direct        addressability of the device's ICP whether or not the device's        target programmatic interface is published in an SDK.    -   Writing control point settings to a device or the device's ICP        as ready-to-run experiments in native file and data formats        using file-less data transfer protocols.    -   Reading data from a device or the device's ICP using file-less        data transfer protocols.    -   Converting a user analysis template into a customized analysis        template that auto-completes when the data are automatically        retrieved from a device or the device's ICP via a dynamically        configurable reporting interface.    -   Converting a user reporting template document (Microsoft Word,        RTF, TXT, or HTML) into a customized reporting template that        auto-completes when the data are automatically retrieved from a        device or the device's ICP via a dynamically configurable        reporting interface.

AEP with GEM—Operational Flow Diagram

FIG. 5A presents an AEP and GEM operational flow diagram illustrating(a) XML exchange with a third-party company, and (b) resulting AEP forthe surrogate data generator and GEM process flows with the instrumentcompany's ICP, in accordance with a preferred embodiment of theinvention. Referring to FIG. 5A, the flow diagram illustrates thatcompany 1 (S-Matrix Corporation) publishes XML schema for the AE system,via step 502. Then a device company, for example, ABC Corporation,creates experiment, analysis, and reporting settings AE XMLs and devicedescription ICP XMLs according to the AE system XML schema, via step504. A GEM process imports device, experiment and analysis templates,via step 505. A GEM process may import report templates, via step 507.The AEP kernel process carries out design, analysis, graphing andoptimization, via step 508. The GEM process then exports design to theICP as ready-to-run in ICP's native data/file formats, via step 512. TheICP executes the desired experiment on the instrument, and collectsresults data (results may also be streamed directly to AE system fromthe device), via step 514. The ICP then sends the data and results tothe GEM process, which imports the data and results to the AE systemusing file-less data exchange, via step 516. The AEP kernel processescarry out analysis, graphing and optimization, again via step 508. TheAEP process then produces surrogate data via the method of the presentinvention, as a surrogate data generator, including the use ofintegrated peak data, via step 510.

AEP with GEM—Software Installation Configurations

FIGS. 6-8 show different software installation configurations of the AEsystem (AEP and GEM) with an ICP. FIG. 6 shows a standalone workstationconfiguration 600. The standalone workstation configuration 600 includesa standalone PC 602 linked to the ICP 604, the servers 608 a and 608 b,and the AE system and ICP 400′. FIG. 7 shows a network configuration 700in which the ICP is a Client/Server (C/S) application 704, linked to theICP main server 708 and to another server 706. FIG. 8 shows analternative network configuration 800 in which the ICP is a C/Sapplication 802 and both the AE system and the ICP 400″ are served touser PCs 804 a and 804 b via a client application such as the Citrix®MetaFrame application 808.

Accordingly, AEP and GEM cooperate to automate the formerly manuallyintensive research, development, and engineering experiments. Toillustrate this in more detail refer now to the following discussionwhich is based on one target application area using an exampleembodiment in accordance with the present invention in conjunction withthe accompanying figures.

FIG. 9 illustrates the research, development, or engineering experimentworkflow previously presented in FIG. 1 adapted to an HPLC methodvalidation experiment created within the AE system's central softwareenvironment and automatically transferred to an instrument's ICP. Thisprovides for an automated experiment construction and file-less exportto ICP as ready-to-run in the ICP's native data and file formats. FIG.10 illustrates the research, development, or engineering experimentworkflow previously presented in FIG. 1, but again carried out withinthe AE system's central software environment. This provides forautomated experiment running and file-less import from ICP as completedresults data sets. The AE system provides an integrated framework tocarry out all the required experiment activities without manuallytranscribing experiments or manually transferring data betweenenvironments.

To maintain data exchange security, the AE system in a preferredembodiment contains a complete 21 CFR 11 (Title 21, Part 11, Code ofFederal Regulations) Regulatory Compliance feature set that enablesmaintenance of regulatory compliance across technology platforms.Example regulatory compliance features include:

-   -   E-signature controls for data exchanges between instrument        platforms.    -   Full audit trail and event logging for all user/software        operations.    -   Automated e-Review and e-Approvals.    -   Full audit trail and event logging for all data events and        reports.

In addition, the AE system provides a complete workflow managementfeature set that enables construction of work templates andsoftware-based administration and control of the work. Example workflowmanagement features include:

-   -   Ability to create and distribute workflow templates.    -   Control of feature/function access with user permissions and        authorities settings.    -   Control of workflow with review and approve e-signing control        loops.    -   E-signature control of all data exchanges with ICPs.

Additional Preferred Aspects and Embodiments

In one aspect of the present invention, the HPLC method developmentexperiment is a system comprising one or more of hardware and softwarecomponents that compute two types of unique surrogate responses, fromstandard HPLC results data sets, being: Peak Count based responses andPeak Result based responses. Two examples of Peak Count based surrogateresponses are Total Peaks and No. of Peaks. As used herein the “TotalPeaks” surrogate response operates to automatically determine the totalnumber of peaks in a chromatogram. The Total Peaks number is typicallythe number of “integrated” peaks obtained from a chromatogram that hasbeen reprocessed based on user set minimum peak height and/or minimumpeak area thresholds. As used herein the “No. of Peaks” surrogateresponse, when linked to an existing result such as peak resolution byan operator such as ≧ and a value of X, will automatically determine thenumber of peaks in a chromatogram with resolution ≧X. One should realizethat multiple responses can be computed at a time for various values ofX, the value of X can be made settable by the user and the default Xvalue for HPLC applications is typically but not restrictively set at1.5. Two examples of Peak Result based operators are Max Peak # and LastPeak. As used herein, when the “Max Peak #” surrogate response is linkedto an existing result such as peak resolution and assigned a value ofone (1) for #, it will automatically locate the largest peak in a givenchromatogram, using peak area as the basis of identifying the largestpeak, and automatically retrieve the resolution result for that peak.Likewise, when the “Last Peak” surrogate response is linked to anexisting result such as peak resolution, it will automatically locatethe last eluting peak in a given chromatogram, and automaticallyretrieve the resolution result for that peak.

For the present invention, references to these unique surrogateresponses are termed “Trend Responses”, since these data contain theinformation on the key trends in chromatographic quality as theexperiment variable settings are systematically changed across theexperiment trials. For the present invention, since the key trends arein terms of chromatographic characteristics most consequential to theability to accurately measure compound amount in a sample, the trendresponses directly support the standard HPLC method development goals.It should be noted that computing the trend responses defined above forthe present invention does not require any assignments of peaks tosample compounds in the chromatogram providing the source data for thecomputation.

In a preferred embodiment of the present invention, the inventionincludes a software solution. FIG. 11 is an example of a software dialogused to input a user settable value of X as the basis of computing theResolved Peaks trend response.

In FIG. 11 a wizard 1100 dialogue is set forth in which results datafrom experimentation is sought to be obtained from instrumentation andother devices in communication relation with the wizard 1100. A userusing the wizard 1100 imports experimental results data automatically,as instructed via the wizard dialogue, from software and relatedinstrumentation platforms associated with the wizard 1100. Theimportation of identified data of interest is determined by a user byselectively setting and choosing data gathering characteristics such ascompound information 1110, and response data information 1120, forinstance but not limitation. Once these results characteristics aredefined, the present invention both automatically gathers data andinformation in relation to the results characteristics sought and isable to automatically adjust settings of connected systeminstrumentation and software applications for each relevant experimentaltrial.

In support of the present invention, Fusion AE can automatically computethe trend responses from integrated peak data available in mostchromatography data system (CDS) software. In the present invention,Fusion AE can automatically obtain the integrated peak data from theprogrammatic interface of the CDS. Thereafter, operatively, fromanalyses of the trend response data sets, the determination andimportation of which is also supported by wizard 1100 dialog using forexample the response data information and resolution thresholds data,Fusion AE determines the best performing combination of the experimentstudy factors.

In a preferred embodiment of the present invention, the inventionincludes a functional flow application, such as that combining hardwareand software components in communication like the Fusion AE SoftwareProgram and an associated Instrument Control Program of an HPLC System,but not necessarily limited thereto, as depicted in FIG. 12. FIG. 12presents the operational functional flow 1200 of the invention in apreferred embodiment including the HPLC method development experiment.

In FIG. 12, from the process flow 1200, using the software methodologyof the present invention a user selects the instrument parameters to usein the experiment of interest at 1210. Once selected, the user thengenerates an experimental design in view of the desired experiment andtrials sought at 1220. At 1230, the user exports the design to HPLCinstrument control program (ICP) in which the HPLC settings areelectronically adjusted between trials in response to settings receivedfrom the user selected study parameters of 1210.

At 1240, the HPLC instrumentation performs the desired experiments inaccordance with the predetermined settings information received from theuser via the software instructions. At 1250, the ICP automaticallydetects and communicates data associated with absorbance measurementsmade during each experimental run of the HPLC. Additionally andpreferably, at 1250, data associated with a chromatogram is alsogenerated automatically in response to the received settingsinformation.

At 1260, the user is able to query the ICP for the available data (i.e.,raw chromatogram data) and set via the software the desired trendresponses sought. Once acquired, the raw data are automaticallyconverted into chromatogram (i.e., chromatogram) data from the ICP andgenerates the response data sets at 1270.

As is used herein, the terms “settings,” “instrument parameters,” “HPLCsettings,” “predetermined settings,” “software instructions,” and “data”may include but are not limited to controllable settings and studyfactor settings as used throughout and herein. Such terms may referencecompound choices, selective blends, proportional aspects of selectcharacteristics, feed rates, temperature gradients, and any controllableelement of a process step in view of settable characteristics ofinstrumentation, hardware and/or software associated with theexperiment.

As used herein the term “trend responses” is intended to meaninformation that comprises one or more of the four properties ofsurrogate responses required to overcome the systemic limitationsinherent in HPLC method development experiments described previously,including data content that: (1) is numerically analyzable; (2) is moreeasily or directly obtained than the current practice results data inwhich inherent data loss occurs; (3) is usable without the need forassociating each peak with a compound (peak tracking) in each of theexperiment chromatograms which would otherwise have been required toprovide the required data on instrument change effects and which wouldin many cases not be available due to the inherent data loss associatedwith peak overlap; (4) provides a response data value for eachexperiment trial undertaken; and/or (5) provides information on theeffects of the changes made to the process or system that would havebeen obtainable if the experiment samples had no inherent data loss.Further, it is intended definitionally to be that a trend responsemeasures a characteristic of the experimental system that will normallybe present and expressed in each experiment trial. For example and notby way of limitation, in HPLC method development, one or more absorbancepeaks will normally be present in the chromatogram result obtained foreach experiment trial, so there will normally be no inherent missingtrend response data. However, under certain operating conditions thatmay be represented in an experiment trial a chromatogram result may beobtained in which no peaks are present. In this case valid trendresponse data will still be computed from the chromatogram result.

Exemplary Experimental Trend Response of the Present Invention

FIG. 15 presents examples of Peak Count based Trend Response datacomputed in an experimental use scenario of the present inventionimplementing Fusion AE from the HPLC method development experimentdiscussed previously. It is evident from FIG. 15, that as opposed to themissing impurities resolution data problem associated with the currentpractice data set, FIG. 15 has a result for each displayed trendresponse for each run.

FIG. 16 presents the regression analysis results for the Total Peakstrend response. FIG. 16 sets forth two critical results in particular.First, all equation (study parameter effect) terms are statisticallysignificant. This is seen from the significance test values associatedwith each term in the table (P-Value less than 0.0500, F-Ratiovalue >4.0000, zero outside the 95% confidence interval). Second, allstudy parameters are represented in the equation in a form related tothe nature of their effects (nonlinear, interaction, etc). From FIG. 16,a ranking of the effect coefficients identifies the largest effect asdue to changing columns (Column B in the table represents the effect ofswitching from Column A to Column B). These results also aredemonstrative that a predictive equation was able to be derived from theTotal Peaks trend response surrogate data that numerically relates thestudy parameter effects to one key aspect of compound separation—thevisualization of all compounds present in the sample.

FIG. 17 presents the regression analysis results for the Resolved Peaks(>1.50) trend response. As for the Total Peaks response all equation(study parameter effect) terms are statistically significant, and allstudy parameters are represented in the equation in a form related tothe nature of their effects (nonlinear, interaction, etc). These resultsare also demonstrative that a predictive equation was able to be derivedfrom the Resolved Peaks trend response surrogate data that numericallyrelates the study parameter effects to a second key aspect of compoundseparation—the separation of each compound from all other compounds tothe extent required.

It should be evident to one of ordinary skill in the art that these twoPeak Count based trend responses are complimentary to the key analyticalmethod development goals of visualizing and adequately separating allcompound peaks present in the sample, and achieving one goal does notnecessarily guarantee that the other goal will also be achieved. Forexample, the best instrument settings for the Total Peaks response mayresult in peaks being present for all compounds, but only some compoundsbeing separated to the degree required. Conversely, the best instrumentsettings for the Resolved Peaks response may resolve almost allcompounds but leave two or more peaks completely unresolved.

In the HPLC method development embodiment the Trend Response approachwill not necessarily yield the optimum HPLC method (instrument parametersettings) in a single experiment, and indeed in this embodiment it isnot meant to. The Trend Response approach of the present invention is aphased approach in which the trend responses enable the experimenter toidentify the best settings of parameters such as Column Type and pH;parameters that normally have the greatest effect on separation andtherefore cause the most inherent data loss. Once these settings areidentified, these parameters are then held constant in a secondexperiment to optimize the HPLC instrument method.

Although the present invention has been described in accordance with theembodiments shown, one of ordinary skill in the art will readilyrecognize that there could be variations to the embodiments and thosevariations would be within the spirit and scope of the presentinvention. Accordingly, many modifications may be made by one ofordinary skill in the art without departing from the spirit and scope ofthe appended claims.

1-20. (canceled)
 21. An automated system for reducing inherent data lossassociated with experimentation that produces one or more uniquesurrogate response data sets, the system comprising: an automatedexperimentation platform (AEP) for automating one or moreexperimentation processes; a generalized exchange module (GEM) forautomating data exchanges between the AEP and target applications andfor enabling the data exchanges to be generic with one or more attachedcomponents; and a software application for predetermining study factorsfor the experimentation, wherein the one or more unique surrogateresponse data sets each are readily obtainable when the inherent dataloss occurs.
 22. The system of claim 21, wherein the one or moreattached components include any of instrumentation, device, softwareapplication, and instrument control program (ICP).
 23. The system ofclaim 21, wherein the one or more unique surrogate response data setseach have primary characteristics that include any of being numericallyanalyzable, being a response value for an experiment trial, andproviding information on effects of changes made to the study factors.24. The system of claim 21, wherein the study factors are inclusive ofone or more controllable settings.
 25. The system of claim 24, whereinthe controllable settings include any of instrument settings,controllable features of instrumentation, variable characteristics ofexperimentation, processes of experimentation, and experimental factorsin accordance with the experimentation.
 26. The system of claim 21,wherein the software application is in communication with theexperimentation.
 27. The system of claim 21, further comprising: aliquid chromatograph (LC), wherein the LC is any of a high-performanceliquid chromatograph (HPLC) and an ultra-high performance liquidchromatograph (UHPLC).
 28. The system of claim 24, further comprising amethod of computing surrogate responses, the method comprising: settingone or more results characteristics in response to the controllablesettings; obtaining one or more results data sets from a data source ofthe system; and determining unique surrogate responses to include any ofPeak Count based surrogate responses and Peak Result based surrogateresponses.
 29. The system of claim 28, wherein the Peak Count basedsurrogate responses include a Total Peaks surrogate response and aNumber of Peaks surrogate response, further wherein: the Total Peakssurrogate response automatically determines a total number of“integrated” peaks obtained from a chromatogram that has beenreprocessed based on any of user set minimum peak height and areathresholds; and the Number of Peaks surrogate response, when linked toan existing result such as peak resolution by an operator such as ≧ anda value of X, will automatically determine a number of peaks in achromatogram with resolution ≧X.
 30. The system of claim 29 wherein thePeak Result based surrogate responses include a Max Peak # surrogateresponse and a Last Peak surrogate response, further wherein: the MaxPeak # surrogate response links to an existing result such as peakresolution and is assigned a value of one (1) for #, wherein Max Peak“1” automatically locates a largest peak in a chromatogram using peakarea and retrieves a resolution result for the located largest peak; andthe Last Peak surrogate response links to an existing result such aspeak resolution, automatically locates a last eluted peak in achromatogram, and retrieves a resolution result for the last elutedpeak.
 31. The system of claim 30, wherein the method further comprises:obtaining multiple surrogate responses using one or more predeterminedtrend response operators with associated values of X, wherein the valueof X is any of a default, predetermined, or user entered value.
 32. Thesystem of claim 31, wherein the value of X is determinable by a userthrough a graphical user interface.
 33. The system of claim 31, furthercomprising: a wizard application in which the system is operable inresponse to selection of one or more controllable settings of resultscharacteristics determined by a user.
 34. The system of claim 29,wherein the surrogate responses are trend responses havingchromatographic quality.
 35. The system of claim 34, wherein the trendresponses are provided in terms of compound separation for improvedaccuracy in measurement of one or more compound amounts in a sample forthe experimentation and are in direct relation to HPLC methoddevelopment.
 36. The system of claim 33, wherein the method furthercomprises: defining one or more controllable settings in the wizardapplication; automatically gathering data information in relation to thedefined controllable settings; and determining automatically anadjustment to the one or more attached components for each relevantexperimental trial of the experimentation.
 37. The system of claim 36,wherein the method further comprises: determining an optimum performingcombination of any of controllable settings and study factors for a nexttrial of the experimentation.
 38. The system of claim 37, wherein theexperimentation includes any of research, development, scientific, andengineering processes.
 39. The system of claim 38, wherein theexperimentation includes processes to separate compounds and determine apresence or absence of impurities.
 40. The system of claim 39, furthercomprising a Fusion AE component.